175 lines
7.2 KiB
Matlab
175 lines
7.2 KiB
Matlab
function [info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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%[info, info_irf, info_moment, data_irf, data_moment] = endogenous_prior_restrictions(T,R,M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% Check for prior (sign) restrictions on irf's and theoretical moments
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% INPUTS
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% T [double] n*n state space matrix
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% R [double] n*k matrix of shocks
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% M_ [structure]
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% options_ [structure]
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% dr [structure] Reduced form model.
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% endo_steady_state [vector] steady state value for endogenous variables
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% exo_steady_state [vector] steady state value for exogenous variables
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% exo_det_steady_state [vector] steady state value for exogenous deterministic variables
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%
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% OUTPUTS
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% info [double] check if prior restrictions are matched by the
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% model and related info
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% info_irf [double] array of test checks for all individual irf restrictions
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% info_moment [double] array of test checks for all individual moment restrictions
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%
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% Copyright © 2013-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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info=[0 0];
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info_irf=[];
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info_moment=[];
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data_irf=[];
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data_moment=[];
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endo_prior_restrictions.irf= options_.endogenous_prior_restrictions.irf;
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endo_prior_restrictions.moment= options_.endogenous_prior_restrictions.moment;
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if ~isempty(endo_prior_restrictions.irf)
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data_irf=cell(size(endo_prior_restrictions.irf,1),1);
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if options_.order>1
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error('The algorithm for prior (sign) restrictions on irf''s is currently restricted to first-order decision rules')
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end
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varlist = M_.endo_names(dr.order_var);
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if isempty(T)
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[T,R,~,~,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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else % check if T and R are given in the restricted form!!!
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if size(T,1)<length(varlist)
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varlist = varlist(dr.restrict_var_list);
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end
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% check if endo_prior_restrictions.irf{:,1} variables are in varlist
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varlistok=1;
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for j=1:size(endo_prior_restrictions.irf,1)
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if isempty(strmatch(endo_prior_restrictions.irf{j,1}, varlist, 'exact'))
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varlistok=0;
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end
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end
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if ~varlistok
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varlist = M_.endo_names(dr.order_var);
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[T,R,~,~,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
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end
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end
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NT=1;
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for j=1:size(endo_prior_restrictions.irf,1)
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NT=max(NT,max(endo_prior_restrictions.irf{j,3}));
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end
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info_irf=ones(size(endo_prior_restrictions.irf,1),2);
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for t=1:NT
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if ~options_.relative_irf
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RR = T^(t-1)*R*diag(sqrt(diag(M_.Sigma_e)));
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else
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RR = T^(t-1)*R*100;
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end
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for j=1:size(endo_prior_restrictions.irf,1)
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if endo_prior_restrictions.irf{j,3}~=t
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continue
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end
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iendo=strmatch(endo_prior_restrictions.irf{j,1}, varlist, 'exact');
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iexo=strmatch(endo_prior_restrictions.irf{j,2}, M_.exo_names, 'exact');
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data_irf{j}=[data_irf{j}; [t RR(iendo,iexo)]];
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if (RR(iendo,iexo)>endo_prior_restrictions.irf{j,4}(1)) && (RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(2))
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info_irf(j,:)=info_irf(j,:).*[0, 0];
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else
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if RR(iendo,iexo)<endo_prior_restrictions.irf{j,4}(1)
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(1))^2;
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else
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delt = (RR(iendo,iexo)-endo_prior_restrictions.irf{j,4}(2))^2;
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end
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info_irf(j,:)=info_irf(j,:).*[49, delt];
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end
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end
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end
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if any(info_irf)
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info=[49,sum(info_irf(:,2))];
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end
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end
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if ~isempty(endo_prior_restrictions.moment)
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if options_.order>1
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error('The algorithm for prior (sign) restrictions on moments is currently restricted to first-order decision rules')
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end
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data_moment=cell(size(endo_prior_restrictions.moment,1),1);
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var_list_ = endo_prior_restrictions.moment{1,1};
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for j=1:size(endo_prior_restrictions.moment,1)
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tmp=endo_prior_restrictions.moment{j,1};
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if ~ismember(tmp,cellstr(var_list_))
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var_list_ = char(var_list_, tmp);
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end
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tmp=endo_prior_restrictions.moment{j,2};
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if ~ismember(tmp,cellstr(var_list_))
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var_list_ = char(var_list_, tmp);
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end
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end
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NTmax=0;
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NTmin=0;
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for j=1:size(endo_prior_restrictions.moment,1)
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NTmax=max(NTmax,max(endo_prior_restrictions.moment{j,3}));
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NTmin=min(NTmin,min(endo_prior_restrictions.moment{j,3}));
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end
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info_moment=ones(size(endo_prior_restrictions.moment,1),2);
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nvar = size(var_list_,1);
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ivar=zeros(nvar,1);
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for i=1:nvar
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i_tmp = strmatch(var_list_(i,:), M_.endo_names, 'exact');
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if isempty(i_tmp)
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error ('One of the variable specified does not exist')
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else
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ivar(i) = i_tmp;
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end
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end
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options_.ar = max(abs(NTmin),NTmax);
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gamma_y = th_autocovariances(dr, ivar, M_, options_,1);
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for t=NTmin:NTmax
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RR = gamma_y{abs(t)+1};
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if t==0
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RR = RR./(sqrt(diag(RR))*sqrt(diag(RR))')-eye(nvar)+diag(diag(gamma_y{t+1})); % becomes correlation
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end
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for j=1:size(endo_prior_restrictions.moment,1)
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if endo_prior_restrictions.moment{j,3}~=t
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continue
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end
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iendo1 = strmatch(endo_prior_restrictions.moment{j,1},var_list_,'exact');
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iendo2 = strmatch(endo_prior_restrictions.moment{j,2},var_list_,'exact');
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if t>0
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tmp0 = iendo1;
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iendo1=iendo2;
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iendo2=tmp0;
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end
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data_moment{j}=[data_moment{j}; [t RR(iendo1,iendo2)]];
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if (RR(iendo1,iendo2)>endo_prior_restrictions.moment{j,4}(1)) && (RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(2))
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info_moment(j,:)=info_moment(j,:).*[0, 0];
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else
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if RR(iendo1,iendo2)<endo_prior_restrictions.moment{j,4}(1)
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delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(1))^2;
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else
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delt = (RR(iendo1,iendo2)-endo_prior_restrictions.moment{j,4}(2))^2;
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end
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info_moment(j,:)=info_moment(j,:).*[49, delt];
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end
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end
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end
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if any(info_moment)
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info=[49, info(2) + sum(info_moment(:,2))];
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end
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end
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